Abstract

We present a method to detect the regions of interests in moving camera views of dynamic scenes with multiple mov- ing objects. We start by extracting a global motion tendency that reflects the scene context by tracking movements of objects in the scene. We then use Gaussian process regression to represent the extracted motion tendency as a stochastic vector field. The generated stochastic field is robust to noise and can handle a video from an uncalibrated moving camera. We use the stochastic field for predicting important future regions of interest as the scene evolves dynamically.

We evaluate our approach on a variety of videos of team sports and compare the detected regions of interest to the camera motion generated by actual camera operators. Our experimental results demonstrate that our approach is computationally efficient, and provides better prediction than those of previously proposed RBF-based approaches.

Analysis and Recognition of motions and activities of objects in videos requires effective representations for analysis and matching of motion trajectories. In this paper, we introduce a new representation speciﬁcally aimed at matching motion trajectories. We model a trajectory as a continuous dense ﬂow ﬁeld from a sparse set of vector sequences using Gaussian Process Regression. Furthermore, we introduce a random sampling strategy for learning stable classes of motions from limited data.

Our representation allows for incrementally predicting possible paths and detecting anomalous events from online trajectories. This representation also supports matching of complex motions with acceleration changes and pauses or stops within a trajectory. We use the proposed approach for classifying and predicting motion trajectories in trafﬁc monitoring domains and test on several data sets. We show that our approach works well on various types of complete and incomplete trajectories from a variety of video data sets with different frame rates

Kitware has received a $13,883,314 contract from Defense Advanced Research Projects Agency (DARPA) to develop a software system capable of automatically and interactively discovering actionable intelligence from wide area motion imagery (WAMI) of complex urban, suburban, and rural environments.

The primary information elements in WAMI data are moving entities in the context of roads, buildings, and other scene features. These entities, while exploitable, often yield fragmented tracks in complex urban environments due to occlusions, stops, and other factors. Kitware’s software system will use algorithmic solutions to associate tracks and then identify and integrate local events to detect potential threats and perform forensic analysis.

The developed algorithms will form the basis of a software prototype called the Persistent Stare Exploitation and Analysis System (PerSEAS) that will significantly augment an end-user’s ability to discover novel intelligence using models of activities, normalcy, and context. Since the vast majority of events are normal and pose no threat, the models must cross-integrate singular events to discover relationships and anomalies that are indicative of suspicious behavior or match previously learned – or defined – threat activity.

The advanced PerSEAS system will markedly improve an analyst’s ability to handle burgeoning WAMI data and reduce the time required to perform many current exploitation tasks, greatly enhancing the military’s capability to analyze and utilize the data for forensic analysis and through the issuance of timely threat alerts with a minimal number of false alarms.

Due to the complex, multi-disciplinary nature of the research, Kitware will partner with academic experts in the fields of computer vision, probabilistic reasoning, machine learning and other related domains. Phase I of the research is expected to be completed in two years.

The awarded contract will expand Kitware’s leadership in the field of computer vision, video analysis and advanced visualization software. The project will build upon our previous DARPA-sponsored research into content-based video retrieval on the VIRAT program; anomaly detection on the PANDA program; and the recognition of complex multi-agent activities in video.

To meet the PerSEAS program’s needs, Kitware has assembled a world-class team including four leading defense technology companies, Northrop Grumman Corporation, ; Honeywell Automation and Control Solutions Laboratories, Aptima, Inc., and Navia, Inc. As well as multiple internationally-renowned research institutions, including: the University of California, Berkeley; Computer Vision Laboratory, University of Maryland; Rensselaer Polytechnic Institute; the Computer Vision Lab at the University of Central Florida; the School of Interactive Computing at Georgia Tech and its affiliated Center for Robotics & Intelligent Machines; and Columbia University.

The Persistent Stare Exploitation and Analysis System (PerSEAS) program is developing the capability to automatically and interactively identify potential threats as they emerge based on the correlation of multiple disparate activities and events in wide area motion imagery (WAMI) and multi-INT data. PerSEAS will enable new methods of threat hypothesis adjudication and forensic analysis through activity-based modeling and inferencing capabilities.